rowdy | A Rocket based JSON Web Token authentication server | Authentication library

Try the below regex

(class\s?=\s?)\"([\d\w\s-])(\brow\b)([\d\w\s])\"

Tested all the cases you mentioned

https://regex101.com

Here is a simple search filter:

I added a search state to help determine whether the user is currently searching or not.

jmg is a numpy array of three values. When you compare a numpy array to another array (or list, in your case), it will compare them element-wise.

You can try to nested a second .map like that :

You are passing wrong props in FlatListItem, now there is no songlist object beacause item itself is a songlist item, So just pass it like this Actions.Play({songIndex: 0, item: this.props.item}) }> and Update inside Play class by replacing this.props.item.songlist to this.props.item

Below is the complete working example

use react-native FlatList to render your title and data both

https://facebook.github.io/react-native/docs/flatlist.html

header support is also available for your title

The problem is that you didn't use self consistently. That line refers to greetings twice; the first time you do use self, but the second time you don't.

Python's "explicit is better than implicit" philosophy means you always need to use self.

Note, though, the code could be simplified to print(random.choice(self.greetings)).

Reading through Weka's StringToWordVector documentation, there seem to be a couple of implementation details different than yours. Here are the top two, based on how likely they are to be the reason for the performance difference you see, in my opinion:

• It seems that by default, the resulting vector is boolean (i.e. noting the existence of a word, rather than number of occurrences)
• If the class attribute is set before vectorizing the text, a separate dictionary is built for each class, then all dictionaries are merged.

While any of them (or other, more minor differences) could be the culprit, my bet is on the second point.

The built-in class allows setting and unsetting each of these options; you could try re-running the 80% version using StringToWordVector with the -C option to use number of occurences rather then a boolean value, and with -O, to use a single dictionary across both classes.

This should allow you to verify whether any of these is indeed the culprit.

EDIT: Regarding the first point, i.e. counting occurences vs. noting word existence (also called Bernoulli and multinomial models), there were several academic papers at the 90s which looked into the differences, e.g. here and here. While usually the multinomial model works better, there are also opposite cases, depending on corpus and classification problem.

One of the problem is you are deleting the row and trying to select image column from the deleted row.. dont use user supplied variables directly in your query

your code should be